Feature engineering plays a critical role in machine learning (ML), but existing methods often struggle with high computational cost and limited scalability when applied to large-scale and sparse datasets. In this article, we propose EaSFE, an efficient and scalable feature engineering framework that unifies feature generation, filtering, and evaluation in an end-to-end manner. EaSFE is designed to efficiently construct and select informative features while explicitly considering computational and memory constraints. To achieve scalability, EaSFE incorporates parallel and distributed execution mechanisms, as well as a chunk-based data processing strategy that enables memory-efficient feature engineering on large datasets. In addition, EaSFE adopts tailored storage and execution strategies to handle high-dimensional sparse data effectively. Extensive experiments on multiple real-world datasets demonstrate that EaSFE consistently improves predictive performance (e.g., 5% accuracy improvement in poker ) while substantially enhancing efficiency (i.e., over 10x speedup) compared to existing feature engineering methods. In addition, EaSFE is demonstrated to scale to large and sparse datasets, successfully handling datasets with over 119 million training instances and 54 million features.

Data science aims to solve real-world problems with the knowledge derived from data. Successfully tackling a data science problem requires practitioners to choose an appropriate solution, which potentially comprises various components such as pre-processing techniques, learning algorithms, hyper-parameters, and so on. Therefore, a problem-driven recommendation for the promising solution is invaluable, as it facilitates efficient and convenient problem-solving. However, existing solution recommendation approaches confront notable challenges when dealing with limited and sparse prior experience in practical applications. Learning from such prior easily leads to overfitting and poor generalization in solution recommendations. To address this issue, we propose a novel solution recommendation method that can predict a good-quality data science solution, including the pre-processing, the learning algorithm, and hyper-parameters, for a given problem. The foundation of our method is a carefully designed ranking model that exploits a weight-sharing structure and a newly proposed loss. The ranking model focuses on incorporating relative ranking information into the predicted performance score of each solution. With these techniques, our method can recommend the solution with the highest score and effectively mitigate the limitations of using sparse prior experience. Our experiments demonstrate the superiority of our method in predicting solutions with higher accuracy and rank, even trained on highly sparse historical performance records. It also reduces recommendation time significantly compared to the baselines, offering remarkable efficiency and convenience for practitioners.

Bandit-based methods are commonly used for hyperparameter optimization (HPO), which is significant in data analytics. When confronted with numerous configurations and high-dimensional large problems, existing bandit-based methods face challenges of high evaluation cost and poor optimization performance. To address these challenges, we introduce an improved bandit-based approach that exhibits enhanced evaluation ability and is suitable for situations with limited resources. Specifically, our method first effectively utilizes the feature and label information to conduct representative groups for further evaluation. After that, two kinds of folds (i.e., general folds and special folds) are constructed to facilitate better evaluation of the configuration in the cross-validation process. Additionally, we incorporate variance and subset size into the evaluation metric to comprehensively evaluate the configuration. We integrate our proposed method into three commonly used bandit-based methods, and experimental results on multiple datasets show that our method has advantages in stability with accuracy improvement of 1 % to 15 % on the datasets tested. In addition, since our method can avoid configurations that are low-quality but time-consuming to evaluate, it is always more efficient than the existing bandit-based methods, and can even reduce the execution time by half in some datasets. Sometimes it takes a little more time, but the improvement in accuracy can be significant.

Heterogeneous Information Network(HIN) collective classification aims to classify one type of node, which is associated with multiple types of nodes through multiple types of relations. Previous studies have revealed that exploiting the relative importance of relation types is quite useful for improving node classification performance. We propose a Tensor-based Markov chain (T-Mark) model to improve the nodes classification accuracy by predicting the labels for unlabeled nodes and the importance ranking of relationship types automatically and simultaneously. Specifically, we build two tensor equations according to the HIN structure and content similarities among nodes of both labeled and unlabeled data. Consequently, We solve the semi-supervised T-Mark model by using an iterative process until obtaining two stationary distributions for labels and relation types. Experimental results on several real-world datasets demonstrate the effectiveness of T-Mark.

Choosing a decomposition method for multi-class classification is an important trade-off between efficiency and predictive accuracy. Trying all the decomposition methods to find the best one is too time-consuming for many applications, while choosing the wrong one may result in large loss on predictive accuracy. In this paper, we propose an automatic decomposition method selection approach called “D-Chooser”, which is lightweight and can choose the best decomposition method accurately. D-Chooser is equipped with our proposed difficulty index which consists of sub-metrics including distribution divergence, overlapping regions, unevenness degree and relative size of the solution space. The difficulty index has two intriguing properties: 1) fast to compute and 2) measuring multi-class problems comprehensively. Extensive experiments on real-world multi-class problems show that D-Chooser achieves an accuracy of 80.56% in choosing the best decomposition method. It can choose the best method in just a few seconds, while existing approaches verify the effectiveness of a decomposition method often takes a few hours. We also provide case studies on Kaggle competitions and the results confirm that D-Chooser is able to choose a better decomposition method than the winning solutions.

Second-order optimization has been recently explored in neural network training. However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training. There have been some attempts to address this issue by approximation on the Hessian matrix, which unfortunately degrades the performance of the neural models. In order to tackle this issue, we propose Kernel Stochastic Gradient Descent (Kernel SGD) which solves the optimization problem in a space transformed by the Hessian matrix of the kernel machine. Kernel SGD eliminates the Hessian matrix recomputation in the training and requires a much smaller memory cost which can be controlled via the mini-batch size. We show that Kernel SGD optimization is theoretically guaranteed to converge. Our experimental results on tabular, image and text data confirm that Kernel SGD converges up to 30 times faster than the existing second-order optimization techniques, and achieves the highest test accuracy on all the tasks tested. Kernel SGD even outperforms the first-order optimization baselines in some problems tested in our experiments.

Heterogeneous Information Network (HIN) collecitve classification studies the problem of predicting labels for one type of nodes in a HIN which contains multiple types of nodes multiple types of links among them. Previous studies have revealed that exploiting relative importance of links is quite useful to improve node classification performance as connected nodes tend to have similar labels. Most existing approaches exploit the relative importance of links either by directly counting the number of connections among nodes or by learning the weight of each type of link from labeled data only. However, these approaches either neglect the importance of types of links to the class labels or may lead to overfitting problem. We propose aTensor-basedMarkov chain (T-Mark) approach, which is able to automatically and simultaneously predict the labels for unlabeled nodes and give the relative importance of types of links that actually improve the classification accuracy. Specifically, we build two tensor equations by using the HIN and features of nodes from both labeled and unlabeled data. A Markov chain-based model is proposed and it is solved by an iterative process to obtain the stationary distributions. Theoretical analyses of the existence and uniqueness of such probability distributions are given. Extensive experimental results demonstrate that T-Mark is able to achieve superior performance in the comparison and obtain reasonable relative importance of links.

In recent years, many data mining practitioners have treated deep neural networks (DNNs) as a standard recipe of creating the state-of-the-art solutions. As a result, models like Support Vector Machines (SVMs) have been overlooked. While the results from DNNs are encouraging, DNNs also come with their huge number of parameters in the model and overheads in long training/inference time. SVMs have excellent properties such as convexity, good generality and efficiency. In this paper, we propose techniques to enhance SVMs with an automatic pipeline which exploits the context of the learning problem. The pipeline consists of several components including data aware subproblem construction, feature customization, data balancing among subproblems with augmentation, and kernel hyper-parameter tuner. Comprehensive experiments show that our proposed solution is more efficient, while producing better results than the other SVM based approaches. Additionally, we conduct a case study of our proposed solution on a popular sentiment analysis problem---the aspect term sentiment analysis (ATSA) task. The study shows that our SVM based solution can achieve competitive predictive accuracy to DNN (and even majority of the BERT) based approaches. Furthermore, our solution is about 40 times faster in inference and has 100 times fewer parameters than the models using BERT. Our findings can encourage more research work on conventional machine learning techniques which may be a good alternative for smaller model size and faster training/inference.

Deep learning has brought great progress for the sequential recommendation (SR) tasks. With advanced network architectures, sequential recommender models can be stacked with many hidden layers, e.g., up to 100 layers on real-world recommendation datasets. Training such a deep network is difficult because it can be computationally very expensive and takes much longer time, especially in situations where there are tens of billions of user-item interactions. To deal with such a challenge, we present StackRec, a simple, yet very effective and efficient training framework for deep SR models by iterative layer stacking. Specifically, we first offer an important insight that hidden layers/blocks in a well-trained deep SR model have very similar distributions. Enlightened by this, we propose the stacking operation on the pre-trained layers/blocks to transfer knowledge from a shallower model to a deep model, then we perform iterative stacking so as to yield a much deeper but easier-to-train SR model. We validate the performance of StackRec by instantiating it with four state-of-the-art SR models in three practical scenarios with real-world datasets. Extensive experiments show that StackRec achieves not only comparable performance, but also substantial acceleration in training time, compared to SR models that are trained from scratch. Codes are available at https://github.com/wangjiachun0426/StackRec.

Multi-class SVMs with the probabilistic output (MP-SVMs) are important techniques in pattern recognition. Two key challenges for efficient GPU accelerations for MP-SVM are: (i) many kernel values are repeatedly computed as a binary SVM classifier is trained iteratively, resulting in repeated accesses to the high latency GPU memory; (ii) performing training or estimating probability in parallel requires a much larger memory footprint than the GPU memory. To overcome the challenges, we propose GMP-SVM to reduce high latency memory accesses and memory consumption through batch processing, computation/data reusing and sharing. Experimental results show that our solution (available in https://github.com/Xtra-Computing/thundersvm) outperforms LibSVM by 100 times while retaining the same accuracy.

Recently, many researchers have been working on improving other traditional machine learning algorithms (besides deep learning) using high-performance hardware such as Graphics Processing Units (GPUs). The recent success of machine learning is not only due to more effective algorithms, but also more efficient systems and implementations. In this paper, we propose a novel and efficient solution to multi-class SVMs with probabilistic output (MP-SVMs) accelerated by GPUs. MP-SVMs are an important technique for many pattern recognition applications. However, MP-SVMs are very time-consuming to use, because using an MP-SVM classifier requires training many binary SVMs and performing probability estimation by combining results of all the binary SVMs. GPUs have much higher computation capability than CPUs and are potentially excellent hardware to accelerate MP-SVMs. Still, two key challenges for efficient GPU accelerations for MP-SVM are: (i) many kernel values are repeatedly computed as a binary SVM classifier is trained iteratively, resulting in repeated accesses to the high latency GPU memory; (ii) performing training or estimating probability in a highly parallel way requires a much larger memory footprint than the GPU memory. To overcome the challenges, we propose a solution called GMP-SVM which exploits two-level (i.e., binary SVM level and MP-SVM level) optimization for training MP-SVMs and high parallelism for estimating probability. GMP-SVM reduces high latency memory accesses and memory consumption through batch processing, kernel value reusing and sharing, and support vector sharing. Experimental results show that GMP-SVM outperforms the GPU baseline by two to five times, and LibSVM with OpenMP by an order of magnitude. Also, GMP-SVM produces the same SVM classifier as LibSVM.

In this paper, we study relations ranking and object classification for multi-relational data where objects are interconnected by multiple relations. The relations among objects should be exploited for achieving a good classification. While most existing approaches exploit either by directly counting the number of connections among objects or by learning the weight of each relation from labeled data only. In this paper, we propose an algorithm, TensorRRCC, which is able to determine the ranking of relations and the labels of objects simultaneously. Our basic idea is that highly ranked relations within a class should play more important roles in object classification, and class membership information is important for determining a ranking quality over the relations w.r.t. a specific learning task. TensorRRCC implements the idea by modeling a Markov chain on transition probability graphs from connection and feature information with both labeled and unlabeled objects and propagates the ranking scores of relations and relevant classes of objects. An iterative progress is proposed to solve a set of tensor equations to obtain the stationary distribution of relations and objects. We compared our algorithm with current collective classification algorithms on two real-world data sets and the experimental results show the superiority of our method.

Multi-label classification deals with the problem where each example is associated with multiple class labels. Since the labels are often dependent to other labels, exploiting label dependencies can significantly improve the multi-label classification performance. The label dependency in existing studies is often given as prior knowledge or learned from the labels only. However, in many real applications, such prior knowledge may not be available, or labeled information might be very limited. In this paper, we propose a new algorithm, called Ml-Forest , to learn an ensemble of hierarchical multi-label classifier trees to reveal the intrinsic label dependencies. In Ml-Forest, we construct a set of hierarchical trees, and develop a label transfer mechanism to identify the multiple relevant labels in a hierarchical way. In general, the relevant labels at higher levels of the trees capture more discriminable label concepts, and they will be transferred into lower level children nodes that are harder to discriminate. The relevant labels in the hierarchy are then aggregated to compute label dependency and make the final prediction. Our empirical study shows encouraging results of the proposed algorithm in comparison with the state-of-the-art multi-label classification algorithms under Friedman test and post-hoc Nemenyi test.

The Earth Mover's Distance (EMD) similarity join retrieves pairs of records with EMD below a given threshold. It has a number of important applications such as near duplicate image retrieval and pattern analysis in probabilistic datasets. However, the computational cost of EMD is super cubic to the number of bins in the histograms used to represent the data objects. Consequently, the EMD similarity join operation is prohibitive for large datasets. This is the first paper that specifically addresses the EMD similarity join and we propose to use MapReduce to approach this problem. The MapReduce algorithms designed for generic metric distance similarity joins are inefficient for the EMD similarity join because they involve a large number of distance computations and have unbalanced workloads on reducers when dealing with skewed datasets. We propose a novel framework, named Melody-Join, which transforms data into the space of EMD lower bounds and performs pruning and partitioning at a low cost because computing these EMD lower bounds has a constant complexity. Furthermore, we address two key problems, the limited pruning power and the unbalanced workloads, by enhancing each phase in the Melody-Join framework. We conduct extensive experiments on real datasets. The results show that Melody-Join outperforms the state-of-the-art technique by an order of magnitude, scales up better on large datasets than the state-of-the-art technique, and scales out well on distributed machines.

Recently, some systems allow users to rate and annotate resources, e.g., Movie Lens, and we consider that it provides a way to identify favor tags and annoying tags of a user by integrating user's rating and tags. In this paper, we reveal and elaborate on the limitations of current works on user profiling for personalized search in collaborative tagging systems. Then we propose a new multi-level user profiling model by integrating tags and ratings to achieve personalized search, which can reflect not only the user's favor but also a user's nuisances. To the best of our knowledge, this is the first effort to integrate the ratings and tags to model multi-level user profiles for personalized search.

We propose and study a new type of facility location selection query, the top-k most influential location selection query. Given a set M of customers and a set F of existing facilities, this query finds k locations from a set C of candidate locations with the largest influence values, where the influence of a candidate location c (c in C) is defined as the number of customers in M who are the reverse nearest neighbors of c. We first present a naive algorithm to process the query. However, the algorithm is computationally expensive and not scalable to large datasets. This motivates us to explore more efficient solutions. We propose two branch and bound algorithms, the Estimation Expanding Pruning (EEP) algorithm and the Bounding Influence Pruning (BIP) algorithm. These algorithms exploit various geometric properties to prune the search space, and thus achieve much better performance than that of the naive algorithm. Specifically, the EEP algorithm estimates the distances to the nearest existing facilities for the customers and the numbers of influenced customers for the candidate locations, and then gradually refines the estimation until the answer set is found, during which distance metric based pruning techniques are used to improve the refinement efficiency. BIP only estimates the numbers of influenced customers for the candidate locations. But it uses the existing facilities to limit the space for searching the influenced customers and achieve a better estimation, which results in an even more efficient algorithm. Extensive experiments conducted on both real and synthetic datasets validate the efficiency of the algorithms.